{ "cells": [ { "cell_type": "markdown", "id": "da5198c6", "metadata": {}, "source": [ "(sec-dataset-advanced-examples)=\n", "# Quantify dataset - advanced examples\n", "\n", "```{seealso}\n", "The complete source code of this tutorial can be found in\n", "\n", "{nb-download}`Quantify dataset - advanced examples.ipynb`\n", "```\n", "\n", "Here we will explore a few advanced usages of the Quantify dataset and how it can\n", "accommodate them." ] }, { "cell_type": "code", "execution_count": 1, "id": "64643c33", "metadata": { "mystnb": { "code_prompt_show": "Imports and auxiliary utilities" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "from rich import pretty\n", "\n", "from quantify_core.analysis.calibration import rotate_to_calibrated_axis\n", "from quantify_core.analysis.fitting_models import exp_decay_func\n", "from quantify_core.data import handling as dh\n", "from quantify_core.utilities import dataset_examples\n", "from quantify_core.utilities.dataset_examples import (\n", " mk_nested_mc_dataset,\n", " mk_shots_from_probabilities,\n", " mk_surface7_cyles_dataset,\n", ")\n", "from quantify_core.utilities.examples_support import (\n", " mk_iq_shots,\n", " round_trip_dataset,\n", ")\n", "from quantify_core.utilities.inspect_utils import display_source_code\n", "\n", "pretty.install()\n", "\n", "dh.set_datadir(Path.home() / \"quantify-data\") # change me!" ] }, { "cell_type": "markdown", "id": "b075ee74", "metadata": {}, "source": [ "## Dataset for an \"unstructured\" experiment\n", "\n", "Let's take consider a Surface Code experiment, in particular the one portrayed in\n", "Fig. 4b from one of the papers from DiCarlo Lab {cite}`marques_logical_qubit_2021`.\n", "\n", "For simplicity, we will not use exactly the same schedule, because what matters here\n", "are the measurements. It is difficult to deal with the results of these measurements\n", "because we have a few repeating cycles followed by a final measurement that leaves the\n", "overall dataset \"unstructured\".\n", "\n", "```{figure} /images/surface-7-sched.png\n", ":width: 100%\n", "```\n", "\n", "``````{admonition} Source code for generating this schedule and visualizing it\n", ":class: dropdown, info\n", "\n", "If you want to create and visualize the schedule above using `quantify-scheduler`,\n", "you can use this code:\n", "\n", "```{code-block} python\n", "from quantify_scheduler import Schedule\n", "from quantify_scheduler.operations.gate_library import CZ, Y90, Measure, Reset, X\n", "from quantify_scheduler.visualization.circuit_diagram import circuit_diagram_matplotlib\n", "\n", "def mk_surface7_sched(num_cycles: int = 3):\n", " \"\"\"Generates a schedule with some of the feature of a Surface 7 experiment as\n", " portrayed in Fig. 4b of :cite:`marques_logical_qubit_2021`.\n", "\n", " Parameters\n", " ----------\n", " num_cycles\n", " The number of times to repeat the main cycle.\n", "\n", " Returns\n", " -------\n", " :\n", " A schedule similar to a Surface 7 dance.\n", " \"\"\"\n", "\n", " sched = Schedule(\"S7 dance\")\n", "\n", " q_d1, q_d2, q_d3, q_d4 = [f\"D{i}\" for i in range(1, 5)]\n", " q_a1, q_a2, q_a3 = [f\"A{i}\" for i in range(1, 4)]\n", " all_qubits = q_d1, q_d2, q_d3, q_d4, q_a1, q_a2, q_a3\n", "\n", " sched.add(Reset(*all_qubits))\n", "\n", " for cycle in range(num_cycles):\n", " sched.add(Y90(q_d1))\n", " for qubit in [q_d2, q_d3, q_d4]:\n", " sched.add(Y90(qubit), ref_pt=\"start\", rel_time=0)\n", " sched.add(Y90(q_a2), ref_pt=\"start\", rel_time=0)\n", "\n", " for qubit in [q_d2, q_d1, q_d4, q_d3]:\n", " sched.add(CZ(qC=qubit, qT=q_a2))\n", "\n", " sched.add(Y90(q_d1))\n", " for qubit in [q_d2, q_d3, q_d4]:\n", " sched.add(Y90(qubit), ref_pt=\"start\", rel_time=0)\n", " sched.add(Y90(q_a2), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(Y90(q_a1), ref_pt=\"end\", rel_time=0)\n", " sched.add(Y90(q_a3), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(CZ(qC=q_d1, qT=q_a1))\n", " sched.add(CZ(qC=q_d2, qT=q_a3))\n", " sched.add(CZ(qC=q_d3, qT=q_a1))\n", " sched.add(CZ(qC=q_d4, qT=q_a3))\n", "\n", " sched.add(Y90(q_a1), ref_pt=\"end\", rel_time=0)\n", " sched.add(Y90(q_a3), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(Measure(q_a2, acq_index=cycle))\n", " for qubit in (q_a1, q_a3):\n", " sched.add(Measure(qubit, acq_index=cycle), ref_pt=\"start\", rel_time=0)\n", "\n", " for qubit in [q_d1, q_d2, q_d3, q_d4]:\n", " sched.add(X(qubit), ref_pt=\"start\", rel_time=0)\n", "\n", " # final measurements\n", "\n", " sched.add(Measure(*all_qubits[:4], acq_index=0), ref_pt=\"end\", rel_time=0)\n", "\n", " return sched\n", "\n", "sched = mk_surface7_sched(num_cycles=3)\n", "f, ax = circuit_diagram_matplotlib(sched)\n", "f.set_figwidth(30)\n", "```\n", "``````\n", "\n", "How do we store all the shots for this measurement?\n", "We might want this because, e.g., we know we have an issue with leakage to the second\n", "excited state of a transmon and we would like to be able to store and inspect raw data.\n", "\n", "To support such use-cases we will have a dimension in the dataset for the repeating cycles\n", "and one extra dimension for the final measurement." ] }, { "cell_type": "code", "execution_count": 2, "id": "d2f2ddcf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_iq_shots(\n",
       "    num_shots: int = 128,\n",
       "    sigmas: Union[Tuple[float], NDArray[np.float64]] = (0.1, 0.1),\n",
       "    centers: Union[Tuple[complex], NDArray[np.complex128]] = (-0.2 + 0.65j, 0.7 + 4j),\n",
       "    probabilities: Union[Tuple[float], NDArray[np.float64]] = (0.4, 0.6),\n",
       "    seed: Union[int, None] = 112233,\n",
       ") -> NDArray:\n",
       "    """\n",
       "    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.\n",
       "\n",
       "    Utility to mock the data coming from qubit readout experiments.\n",
       "    Clusters are centered around ``centers`` and data points are distributed between\n",
       "    them according to ``probabilities``.\n",
       "\n",
       "    .. seealso:: :ref:`howto-utilities-examples-ssro`\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_shots\n",
       "        The number of shot to generate.\n",
       "    sigma\n",
       "        The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
       "    centers\n",
       "        The center of each cluster on the imaginary plane.\n",
       "    probabilities\n",
       "        The probabilities of each cluster being randomly selected for each shot.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    if not len(sigmas) == len(centers) == len(probabilities):\n",
       "        raise ValueError(\n",
       "            f"Incorrect input. sigmas={sigmas}, centers={centers} and "\n",
       "            f"probabilities={probabilities} must have the same length."\n",
       "        )\n",
       "\n",
       "    rng = np.random.default_rng(seed=seed)\n",
       "\n",
       "    cluster_indices = tuple(range(len(centers)))\n",
       "    choices = rng.choice(a=cluster_indices, size=num_shots, p=probabilities)\n",
       "\n",
       "    shots = []\n",
       "    for idx in cluster_indices:\n",
       "        num_shots_this_cluster = np.sum(choices == idx)\n",
       "        i_data = rng.normal(\n",
       "            loc=centers[idx].real,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        q_data = rng.normal(\n",
       "            loc=centers[idx].imag,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        shots.append(i_data + 1j * q_data)\n",
       "    return np.concatenate(shots)\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\n",
       "    \\PY{n}{num\\PYZus{}shots}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{128}\\PY{p}{,}\n",
       "    \\PY{n}{sigmas}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{float64}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.1}\\PY{p}{,} \\PY{l+m+mf}{0.1}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{centers}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{complex}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{complex128}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.2} \\PY{o}{+} \\PY{l+m+mf}{0.65}\\PY{n}{j}\\PY{p}{,} \\PY{l+m+mf}{0.7} \\PY{o}{+} \\PY{l+m+mi}{4}\\PY{n}{j}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{float64}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.4}\\PY{p}{,} \\PY{l+m+mf}{0.6}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{seed}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{,} \\PY{k+kc}{None}\\PY{p}{]} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{NDArray}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.}\n",
       "\n",
       "\\PY{l+s+sd}{    Utility to mock the data coming from qubit readout experiments.}\n",
       "\\PY{l+s+sd}{    Clusters are centered around ``centers`` and data points are distributed between}\n",
       "\\PY{l+s+sd}{    them according to ``probabilities``.}\n",
       "\n",
       "\\PY{l+s+sd}{    .. seealso:: :ref:`howto\\PYZhy{}utilities\\PYZhy{}examples\\PYZhy{}ssro`}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}shots}\n",
       "\\PY{l+s+sd}{        The number of shot to generate.}\n",
       "\\PY{l+s+sd}{    sigma}\n",
       "\\PY{l+s+sd}{        The sigma of the Gaussian distribution used for both real and imaginary parts.}\n",
       "\\PY{l+s+sd}{    centers}\n",
       "\\PY{l+s+sd}{        The center of each cluster on the imaginary plane.}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The probabilities of each cluster being randomly selected for each shot.}\n",
       "\\PY{l+s+sd}{    seed}\n",
       "\\PY{l+s+sd}{        Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{k}{if} \\PY{o+ow}{not} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{sigmas}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{k}{raise} \\PY{n+ne}{ValueError}\\PY{p}{(}\n",
       "            \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Incorrect input. sigmas=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{sigmas}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{, centers=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{centers}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ and }\\PY{l+s+s2}{\\PYZdq{}}\n",
       "            \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{probabilities=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{probabilities}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ must have the same length.}\\PY{l+s+s2}{\\PYZdq{}}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{cluster\\PYZus{}indices} \\PY{o}{=} \\PY{n+nb}{tuple}\\PY{p}{(}\\PY{n+nb}{range}\\PY{p}{(}\\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{n}{choices} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{choice}\\PY{p}{(}\\PY{n}{a}\\PY{o}{=}\\PY{n}{cluster\\PYZus{}indices}\\PY{p}{,} \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots}\\PY{p}{,} \\PY{n}{p}\\PY{o}{=}\\PY{n}{probabilities}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{p}{[}\\PY{p}{]}\n",
       "    \\PY{k}{for} \\PY{n}{idx} \\PY{o+ow}{in} \\PY{n}{cluster\\PYZus{}indices}\\PY{p}{:}\n",
       "        \\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{sum}\\PY{p}{(}\\PY{n}{choices} \\PY{o}{==} \\PY{n}{idx}\\PY{p}{)}\n",
       "        \\PY{n}{i\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
       "            \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{real}\\PY{p}{,}\n",
       "            \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
       "            \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{n}{q\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
       "            \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{imag}\\PY{p}{,}\n",
       "            \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
       "            \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{n}{shots}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{i\\PYZus{}data} \\PY{o}{+} \\PY{l+m+mi}{1}\\PY{n}{j} \\PY{o}{*} \\PY{n}{q\\PYZus{}data}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{np}\\PY{o}{.}\\PY{n}{concatenate}\\PY{p}{(}\\PY{n}{shots}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_iq_shots\u001b[0m\u001b[1m(\u001b[0m\n",
       "    num_shots: int = \u001b[1;36m128\u001b[0m,\n",
       "    sigmas: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mfloat\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.float64\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.1\u001b[0m, \u001b[1;36m0.1\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    centers: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mcomplex\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.complex128\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m-0.2\u001b[0m + \u001b[1;36m0.65j\u001b[0m, \u001b[1;36m0.7\u001b[0m + \u001b[1;36m4j\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    probabilities: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mfloat\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.float64\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.4\u001b[0m, \u001b[1;36m0.6\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    seed: Union\u001b[1m[\u001b[0mint, \u001b[3;35mNone\u001b[0m\u001b[1m]\u001b[0m = \u001b[1;36m112233\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> NDArray:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generate clusters of \u001b[1m(\u001b[0mI + \u001b[1;36m1j\u001b[0m*Q\u001b[1m)\u001b[0m points with a Gaussian distribution.\n",
       "\n",
       "    Utility to mock the data coming from qubit readout experiments.\n",
       "    Clusters are centered around ``centers`` and data points are distributed between\n",
       "    them according to ``probabilities``.\n",
       "\n",
       "    .. seealso:: :ref:`howto-utilities-examples-ssro`\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_shots\n",
       "        The number of shot to generate.\n",
       "    sigma\n",
       "        The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
       "    centers\n",
       "        The center of each cluster on the imaginary plane.\n",
       "    probabilities\n",
       "        The probabilities of each cluster being randomly selected for each shot.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    if not \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0msigmas\u001b[1m)\u001b[0m == \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mcenters\u001b[1m)\u001b[0m == \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mprobabilities\u001b[1m)\u001b[0m:\n",
       "        raise \u001b[1;35mValueError\u001b[0m\u001b[1m(\u001b[0m\n",
       "            f\"Incorrect input. \u001b[33msigmas\u001b[0m=\u001b[1m{\u001b[0msigmas\u001b[1m}\u001b[0m, \u001b[33mcenters\u001b[0m=\u001b[1m{\u001b[0mcenters\u001b[1m}\u001b[0m and \"\n",
       "            f\"\u001b[33mprobabilities\u001b[0m=\u001b[1m{\u001b[0mprobabilities\u001b[1m}\u001b[0m must have the same length.\"\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    rng = \u001b[1;35mnp.random.default_rng\u001b[0m\u001b[1m(\u001b[0m\u001b[33mseed\u001b[0m=\u001b[35mseed\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    cluster_indices = \u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mcenters\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "    choices = \u001b[1;35mrng.choice\u001b[0m\u001b[1m(\u001b[0m\u001b[33ma\u001b[0m=\u001b[35mcluster_indices\u001b[0m, \u001b[33msize\u001b[0m=\u001b[35mnum_shots\u001b[0m, \u001b[33mp\u001b[0m=\u001b[35mprobabilities\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    shots = \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    for idx in cluster_indices:\n",
       "        num_shots_this_cluster = \u001b[1;35mnp.sum\u001b[0m\u001b[1m(\u001b[0mchoices == idx\u001b[1m)\u001b[0m\n",
       "        i_data = \u001b[1;35mrng.normal\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mloc\u001b[0m=\u001b[35mcenters\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m.real,\n",
       "            \u001b[33mscale\u001b[0m=\u001b[35msigmas\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m,\n",
       "            \u001b[33msize\u001b[0m=\u001b[35mnum_shots_this_cluster\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "        q_data = \u001b[1;35mrng.normal\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mloc\u001b[0m=\u001b[35mcenters\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m.imag,\n",
       "            \u001b[33mscale\u001b[0m=\u001b[35msigmas\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m,\n",
       "            \u001b[33msize\u001b[0m=\u001b[35mnum_shots_this_cluster\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "        \u001b[1;35mshots.append\u001b[0m\u001b[1m(\u001b[0mi_data + \u001b[1;36m1j\u001b[0m * q_data\u001b[1m)\u001b[0m\n",
       "    return \u001b[1;35mnp.concatenate\u001b[0m\u001b[1m(\u001b[0mshots\u001b[1m)\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_shots_from_probabilities(probabilities: Union[np.ndarray, list], **kwargs):\n",
       "    """Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: (num_shots, len(probabilities)).\n",
       "    """\n",
       "\n",
       "    shots = np.array(\n",
       "        tuple(\n",
       "            mk_iq_shots(probabilities=[prob, 1 - prob], **kwargs)\n",
       "            for prob in probabilities\n",
       "        )\n",
       "    ).T\n",
       "\n",
       "    return shots\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{,} \\PY{n+nb}{list}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Generates multiple shots for a list of probabilities assuming two states.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The list/array of the probabilities of one of the states.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to}\n",
       "\\PY{l+s+sd}{        :func:`\\PYZti{}quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots`.}\n",
       "\n",
       "\\PY{l+s+sd}{    Returns}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    :}\n",
       "\\PY{l+s+sd}{        Array containing the shots. Shape: (num\\PYZus{}shots, len(probabilities)).}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\n",
       "        \\PY{n+nb}{tuple}\\PY{p}{(}\n",
       "            \\PY{n}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{prob}\\PY{p}{,} \\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{prob}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{k}{for} \\PY{n}{prob} \\PY{o+ow}{in} \\PY{n}{probabilities}\n",
       "        \\PY{p}{)}\n",
       "    \\PY{p}{)}\\PY{o}{.}\\PY{n}{T}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{shots}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0mprobabilities: Union\u001b[1m[\u001b[0mnp.ndarray, list\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m:\n",
       "    \u001b[32m\"\"\u001b[0m\"Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: \u001b[1m(\u001b[0mnum_shots, \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mprobabilities\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    shots = \u001b[1;35mnp.array\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[1;35mmk_iq_shots\u001b[0m\u001b[1m(\u001b[0m\u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0mprob, \u001b[1;36m1\u001b[0m - prob\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m\n",
       "            for prob in probabilities\n",
       "        \u001b[1m)\u001b[0m\n",
       "    \u001b[1m)\u001b[0m.T\n",
       "\n",
       "    return shots"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_surface7_cyles_dataset(num_cycles: int = 3, **kwargs) -> xr.Dataset:\n",
       "    """\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    """\n",
       "\n",
       "    cycles = range(num_cycles)\n",
       "\n",
       "    mock_data = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random() for _ in cycles], **kwargs\n",
       "    )\n",
       "\n",
       "    mock_data_final = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random()], **kwargs\n",
       "    )\n",
       "\n",
       "    # %%\n",
       "    data_vars = {}\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in (f"A{i}" for i in range(3)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_cycle"),\n",
       "            mock_data,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    for qubit in (f"D{i}" for i in range(4)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_final"),\n",
       "            mock_data_final,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    cycle_attrs = mk_main_coord_attrs(long_name="Surface code cycle number")\n",
       "    final_msmt_attrs = mk_main_coord_attrs(long_name="Final measurement")\n",
       "    coords = dict(\n",
       "        cycle=("dim_cycle", cycles, cycle_attrs),\n",
       "        final_msmt=("dim_final", [0], final_msmt_attrs),\n",
       "    )\n",
       "\n",
       "    dataset = xr.Dataset(\n",
       "        data_vars=data_vars,\n",
       "        coords=coords,\n",
       "        attrs=mk_dataset_attrs(),\n",
       "    )\n",
       "\n",
       "    return dataset\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}surface7\\PYZus{}cyles\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{3}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    See also :func:`mk\\PYZus{}surface7\\PYZus{}sched` inlined in the documentation as an example in:}\n",
       "\\PY{l+s+sd}{        :ref:`sec\\PYZhy{}dataset\\PYZhy{}advanced\\PYZhy{}examples`}\n",
       "\n",
       "\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}cycles}\n",
       "\\PY{l+s+sd}{        The number of repeating cycles before the final measurement.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to :func:`\\PYZti{}.mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities`.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{cycles} \\PY{o}{=} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n}{cycles}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data\\PYZus{}final} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} \\PYZpc{}\\PYZpc{}}\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{p}{\\PYZob{}}\\PY{p}{\\PYZcb{}}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} NB same random data is used for all qubits only for the simplicity of the mock!}\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{D}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{4}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data\\PYZus{}final}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{cycle\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Surface code cycle number}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Final measurement}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{cycle}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{cycles}\\PY{p}{,} \\PY{n}{cycle\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{final\\PYZus{}msmt}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{,} \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,}\n",
       "        \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,}\n",
       "        \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_surface7_cyles_dataset\u001b[0m\u001b[1m(\u001b[0mnum_cycles: int = \u001b[1;36m3\u001b[0m, **kwargs\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    cycles = \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_cycles\u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in cycles\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data_final = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    # %%\n",
       "    data_vars = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"A\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_cycle\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"D\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_final\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data_final,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    cycle_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Surface\u001b[0m\u001b[32m code cycle number\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    final_msmt_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Final\u001b[0m\u001b[32m measurement\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mcycle\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_cycle\"\u001b[0m, cycles, cycle_attrs\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mfinal_msmt\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_final\"\u001b[0m, \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m, final_msmt_attrs\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m,\n",
       "        \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m,\n",
       "        \u001b[33mattrs\u001b[0m=\u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mock data parameters\n",
    "num_shots = 128  # NB usually >~1000 in real experiments\n",
    "ground = -0.2 + 0.65j\n",
    "excited = 0.7 + 4j\n",
    "centroids = ground, excited\n",
    "sigmas = [0.1] * 2\n",
    "\n",
    "display_source_code(mk_iq_shots)\n",
    "display_source_code(mk_shots_from_probabilities)\n",
    "display_source_code(mk_surface7_cyles_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12829358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> Size: 27kB\n",
       "Dimensions:     (repetitions: 128, dim_cycle: 3, dim_final: 1)\n",
       "Coordinates:\n",
       "    cycle       (dim_cycle) int64 24B 0 1 2\n",
       "    final_msmt  (dim_final) int64 8B 0\n",
       "Dimensions without coordinates: repetitions, dim_cycle, dim_final\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    A1_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    A2_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    D0_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D1_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D2_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D3_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "Attributes:\n",
       "    tuid:                      20250723-134459-036-ab793d\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 27kB\n",
       "Dimensions:     \u001b[1m(\u001b[0mrepetitions: \u001b[1;36m128\u001b[0m, dim_cycle: \u001b[1;36m3\u001b[0m, dim_final: \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    cycle       \u001b[1m(\u001b[0mdim_cycle\u001b[1m)\u001b[0m int64 24B \u001b[1;36m0\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;36m2\u001b[0m\n",
       "    final_msmt  \u001b[1m(\u001b[0mdim_final\u001b[1m)\u001b[0m int64 8B \u001b[1;36m0\u001b[0m\n",
       "Dimensions without coordinates: repetitions, dim_cycle, dim_final\n",
       "Data variables:\n",
       "    A0_shots    \u001b[1m(\u001b[0mrepetitions, dim_cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A1_shots    \u001b[1m(\u001b[0mrepetitions, dim_cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A2_shots    \u001b[1m(\u001b[0mrepetitions, dim_cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D0_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D1_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D2_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D3_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20250723\u001b[0m-\u001b[1;36m134459\u001b[0m-\u001b[1;36m036\u001b[0m-ab793d\n",
       "    dataset_name:              \n",
       "    dataset_state:             \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_start:           \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_end:             \u001b[3;35mNone\u001b[0m\n",
       "    quantify_dataset_version:  \u001b[1;36m2.0\u001b[0m.\u001b[1;36m0\u001b[0m\n",
       "    software_versions:         \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "    relationships:             \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = mk_surface7_cyles_dataset(\n",
    "    num_shots=num_shots, sigmas=sigmas, centers=centroids\n",
    ")\n",
    "\n",
    "assert dataset == round_trip_dataset(dataset)  # confirm read/write\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b71d9599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001b[1m(\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m128\u001b[0m, \u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[1;36m128\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.A1_shots.shape, dataset.D1_shots.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4cf70976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "
<xarray.Dataset> Size: 27kB\n",
       "Dimensions:     (repetitions: 128, cycle: 3, final_msmt: 1)\n",
       "Coordinates:\n",
       "  * cycle       (cycle) int64 24B 0 1 2\n",
       "  * final_msmt  (final_msmt) int64 8B 0\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.5...\n",
       "    A1_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.5...\n",
       "    A2_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.5...\n",
       "    D0_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "    D1_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "    D2_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "    D3_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "Attributes:\n",
       "    tuid:                      20250723-134459-036-ab793d\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 27kB\n",
       "Dimensions:     \u001b[1m(\u001b[0mrepetitions: \u001b[1;36m128\u001b[0m, cycle: \u001b[1;36m3\u001b[0m, final_msmt: \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "  * cycle       \u001b[1m(\u001b[0mcycle\u001b[1m)\u001b[0m int64 24B \u001b[1;36m0\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;36m2\u001b[0m\n",
       "  * final_msmt  \u001b[1m(\u001b[0mfinal_msmt\u001b[1m)\u001b[0m int64 8B \u001b[1;36m0\u001b[0m\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    A0_shots    \u001b[1m(\u001b[0mrepetitions, cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.5\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A1_shots    \u001b[1m(\u001b[0mrepetitions, cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.5\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A2_shots    \u001b[1m(\u001b[0mrepetitions, cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.5\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D0_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D1_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D2_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D3_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20250723\u001b[0m-\u001b[1;36m134459\u001b[0m-\u001b[1;36m036\u001b[0m-ab793d\n",
       "    dataset_name:              \n",
       "    dataset_state:             \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_start:           \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_end:             \u001b[3;35mNone\u001b[0m\n",
       "    quantify_dataset_version:  \u001b[1;36m2.0\u001b[0m.\u001b[1;36m0\u001b[0m\n",
       "    software_versions:         \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "    relationships:             \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset, dimension=\"dim_cycle\", coords_names=[\"cycle\"]\n",
    ")\n",
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset_gridded, dimension=\"dim_final\", coords_names=[\"final_msmt\"]\n",
    ")\n",
    "dataset_gridded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a216dc93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 64\u001b[0m\u001b[1;36m0x480\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_gridded.A0_shots.real.mean(\"repetitions\").plot(marker=\"o\", label=\"I-quadrature\")\n",
    "dataset_gridded.A0_shots.imag.mean(\"repetitions\").plot(marker=\"^\", label=\"Q-quadrature\")\n",
    "_ = plt.gca().legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b4f6af",
   "metadata": {},
   "source": [
    "(sec-nested-mc-example)=\n",
    "## Dataset for a \"nested MeasurementControl\" experiment\n",
    "\n",
    "Now consider a dataset that has been constructed by an experiment involving the\n",
    "operation of two\n",
    "{class}`.MeasurementControl` objects. The second of\n",
    "them performs a \"meta\" outer loop in which we sweep a flux bias and then perform\n",
    "several experiments to characterize a transmon qubit, e.g. determining the frequency of\n",
    "a read-out resonator, the frequency of the transmon, and its T1 lifetime.\n",
    "\n",
    "Below we showcase what the data from the dataset containing the T1 experiment results\n",
    "could look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d839806c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 64\u001b[0m\u001b[1;36m0x480\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "rng = np.random.default_rng(seed=112244)  # random number generator\n",
    "\n",
    "num_t1_datasets = 7\n",
    "t1_times = np.linspace(0, 120e-6, 30)\n",
    "\n",
    "for tau in rng.uniform(10e-6, 50e-6, num_t1_datasets):\n",
    "    probabilities = exp_decay_func(\n",
    "        t=t1_times, tau=tau, offset=0, n_factor=1, amplitude=1\n",
    "    )\n",
    "    dataset = dataset_examples.mk_t1_av_with_cal_dataset(t1_times, probabilities)\n",
    "\n",
    "    round_trip_dataset(dataset)  # confirm read/write\n",
    "    dataset_g = dh.to_gridded_dataset(\n",
    "        dataset, dimension=\"main_dim\", coords_names=[\"t1_time\"]\n",
    "    )\n",
    "    # rotate the iq data\n",
    "    rotated_and_normalized = rotate_to_calibrated_axis(\n",
    "        dataset_g.q0_iq_av.values, *dataset_g.q0_iq_av_cal.values\n",
    "    )\n",
    "    rotated_and_normalized_da = xr.DataArray(dataset_g.q0_iq_av)\n",
    "    rotated_and_normalized_da.values = rotated_and_normalized\n",
    "    rotated_and_normalized_da.attrs[\"long_name\"] = \"|1> Population\"\n",
    "    rotated_and_normalized_da.attrs[\"units\"] = \"\"\n",
    "    rotated_and_normalized_da.real.plot(ax=ax, label=dataset.tuid, marker=\".\")\n",
    "ax.set_title(\"Results from repeated T1 experiments\\n(different datasets)\")\n",
    "_ = ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae491ce5",
   "metadata": {},
   "source": [
    "Since the raw data is now split among several datasets, we would like to keep a\n",
    "reference to all these datasets in our \"combined\" datasets. Below we showcase how this\n",
    "can be achieved, along with some useful xarray features and known limitations.\n",
    "\n",
    "We start by generating a mock dataset that combines all the information that would have\n",
    "been obtained from analyzing a series of other datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7149f12",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Source code for mk_nested_mc_dataset function"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_nested_mc_dataset(\n",
       "    num_points: int = 12,\n",
       "    flux_bias_min_max: tuple = (-0.04, 0.04),\n",
       "    resonator_freqs_min_max: tuple = (7e9, 7.3e9),\n",
       "    qubit_freqs_min_max: tuple = (4.5e9, 5.0e9),\n",
       "    t1_values_min_max: tuple = (20e-6, 50e-6),\n",
       "    seed: int | None = 112233,\n",
       ") -> xr.Dataset:\n",
       "    """\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate (used for all variables/coordinates).\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    rng = np.random.default_rng(seed=seed)  # random number generator\n",
       "\n",
       "    flux_bias_vals = np.linspace(*flux_bias_min_max, num_points)\n",
       "    resonator_freqs = np.linspace(*resonator_freqs_min_max, num_points)\n",
       "    qubit_freqs = np.linspace(*qubit_freqs_min_max, num_points)\n",
       "    t1_values = rng.uniform(*t1_values_min_max, num_points)\n",
       "\n",
       "    resonator_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    qubit_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    t1_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "\n",
       "    coords = dict(\n",
       "        flux_bias=(\n",
       "            "main_dim",\n",
       "            flux_bias_vals,\n",
       "            mk_main_coord_attrs(long_name="Flux bias", unit="A"),\n",
       "        ),\n",
       "        resonator_freq_tuids=(\n",
       "            "main_dim",\n",
       "            resonator_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID resonator frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        qubit_freq_tuids=(\n",
       "            "main_dim",\n",
       "            qubit_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID qubit frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        t1_tuids=(\n",
       "            "main_dim",\n",
       "            t1_tuids,\n",
       "            mk_main_coord_attrs(long_name="Dataset TUID T1", is_dataset_ref=True),\n",
       "        ),\n",
       "    )\n",
       "\n",
       "    data_vars = dict(\n",
       "        resonator_freq=(\n",
       "            "main_dim",\n",
       "            resonator_freqs,\n",
       "            mk_main_var_attrs(long_name="Resonator frequency", unit="Hz"),\n",
       "        ),\n",
       "        qubit_freq=(\n",
       "            "main_dim",\n",
       "            qubit_freqs,\n",
       "            mk_main_var_attrs(long_name="Qubit frequency", unit="Hz"),\n",
       "        ),\n",
       "        t1=(\n",
       "            "main_dim",\n",
       "            t1_values,\n",
       "            mk_main_var_attrs(long_name="T1", unit="s"),\n",
       "        ),\n",
       "    )\n",
       "    dataset_attrs = mk_dataset_attrs()\n",
       "\n",
       "    dataset = xr.Dataset(data_vars=data_vars, coords=coords, attrs=dataset_attrs)\n",
       "\n",
       "    return dataset\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}nested\\PYZus{}mc\\PYZus{}dataset}\\PY{p}{(}\n",
       "    \\PY{n}{num\\PYZus{}points}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{12}\\PY{p}{,}\n",
       "    \\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.04}\\PY{p}{,} \\PY{l+m+mf}{0.04}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{7e9}\\PY{p}{,} \\PY{l+m+mf}{7.3e9}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{4.5e9}\\PY{p}{,} \\PY{l+m+mf}{5.0e9}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{20e\\PYZhy{}6}\\PY{p}{,} \\PY{l+m+mf}{50e\\PYZhy{}6}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{seed}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{|} \\PY{k+kc}{None} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Generates a dataset with dataset references and several coordinates that serve to}\n",
       "\\PY{l+s+sd}{    index the same variables.}\n",
       "\n",
       "\\PY{l+s+sd}{    Note that the each value for ``resonator\\PYZus{}freqs``, ``qubit\\PYZus{}freqs`` and ``t1\\PYZus{}values``}\n",
       "\\PY{l+s+sd}{    would have been extracted from other dataset corresponding to individual experiments}\n",
       "\\PY{l+s+sd}{    with their own dataset.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}points}\n",
       "\\PY{l+s+sd}{        Number of datapoints to generate (used for all variables/coordinates).}\n",
       "\\PY{l+s+sd}{    flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock random values.}\n",
       "\\PY{l+s+sd}{    seed}\n",
       "\\PY{l+s+sd}{        Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}  \\PY{c+c1}{\\PYZsh{} random number generator}\n",
       "\n",
       "    \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{resonator\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{qubit\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{t1\\PYZus{}values} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{uniform}\\PY{p}{(}\\PY{o}{*}\\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{t1\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{flux\\PYZus{}bias}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Flux bias}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{resonator\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}values}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{s}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{dataset\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,} \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,} \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}attrs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_nested_mc_dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "    num_points: int = \u001b[1;36m12\u001b[0m,\n",
       "    flux_bias_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m-0.04\u001b[0m, \u001b[1;36m0.04\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    resonator_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m7e9\u001b[0m, \u001b[1;36m7.3e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    qubit_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m4.5e9\u001b[0m, \u001b[1;36m5.0e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    t1_values_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m20e-6\u001b[0m, \u001b[1;36m50e-6\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    seed: int | \u001b[3;35mNone\u001b[0m = \u001b[1;36m112233\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate \u001b[1m(\u001b[0mused for all variables/coordinates\u001b[1m)\u001b[0m.\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    rng = \u001b[1;35mnp.random.default_rng\u001b[0m\u001b[1m(\u001b[0m\u001b[33mseed\u001b[0m=\u001b[35mseed\u001b[0m\u001b[1m)\u001b[0m  # random number generator\n",
       "\n",
       "    flux_bias_vals = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*flux_bias_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    resonator_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*resonator_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    qubit_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*qubit_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    t1_values = \u001b[1;35mrng.uniform\u001b[0m\u001b[1m(\u001b[0m*t1_values_min_max, num_points\u001b[1m)\u001b[0m\n",
       "\n",
       "    resonator_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    qubit_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    t1_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mflux_bias\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            flux_bias_vals,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Flux\u001b[0m\u001b[32m bias\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"A\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mresonator_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID resonator frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID qubit frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID T1\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mresonator_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Resonator\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Qubit\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_values,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"T1\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"s\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "    dataset_attrs = \u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m, \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m, \u001b[33mattrs\u001b[0m=\u001b[35mdataset_attrs\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(mk_nested_mc_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09512820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> Size: 2kB\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "    flux_bias             (main_dim) float64 56B -0.04 -0.02667 ... 0.02667 0.04\n",
       "    resonator_freq_tuids  (main_dim) <U26 728B '20250723-134500-220-0ee468' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20250723-134500-220-136cde' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20250723-134500-221-80acf5' ....\n",
       "Dimensions without coordinates: main_dim\n",
       "Data variables:\n",
       "    resonator_freq        (main_dim) float64 56B 7e+09 7.05e+09 ... 7.3e+09\n",
       "    qubit_freq            (main_dim) float64 56B 4.5e+09 4.583e+09 ... 5e+09\n",
       "    t1                    (main_dim) float64 56B 4.238e-05 ... 4.154e-05\n",
       "Attributes:\n",
       "    tuid:                      20250723-134500-222-c5d165\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
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      ],
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       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 2kB\n",
       "Dimensions:               \u001b[1m(\u001b[0mmain_dim: \u001b[1;36m7\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 56B \u001b[1;36m-0.04\u001b[0m \u001b[1;36m-0.02667\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.02667\u001b[0m \u001b[1;36m0.04\u001b[0m\n",
       "    resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 100\u001b[0m\u001b[1;36m0x1000\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m4\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(10, 10), sharex=True)\n",
    "\n",
    "_ = dataset.t1.plot(x=\"flux_bias\", marker=\"o\", ax=axs[0].twiny(), color=\"C0\")\n",
    "x = \"t1_tuids\"\n",
    "_ = dataset.t1.plot(x=x, marker=\"o\", ax=axs[0], color=\"C0\")\n",
    "_ = dataset.resonator_freq.plot(x=x, marker=\"o\", ax=axs[1], color=\"C1\")\n",
    "_ = dataset.qubit_freq.plot(x=x, marker=\"o\", ax=axs[2], color=\"C2\")\n",
    "for tick in axs[2].get_xticklabels():\n",
    "    tick.set_rotation(15)  # avoid tuid labels overlapping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c30a1b",
   "metadata": {},
   "source": [
    "It is possible to work with an explicit MultiIndex within a (python) xarray object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d1df2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> Size: 2kB\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "  * main_dim              (main_dim) object 56B MultiIndex\n",
       "  * flux_bias             (main_dim) float64 56B -0.04 -0.02667 ... 0.02667 0.04\n",
       "  * resonator_freq_tuids  (main_dim) <U26 728B '20250723-134500-220-0ee468' ....\n",
       "  * qubit_freq_tuids      (main_dim) <U26 728B '20250723-134500-220-136cde' ....\n",
       "  * t1_tuids              (main_dim) <U26 728B '20250723-134500-221-80acf5' ....\n",
       "Data variables:\n",
       "    resonator_freq        (main_dim) float64 56B 7e+09 7.05e+09 ... 7.3e+09\n",
       "    qubit_freq            (main_dim) float64 56B 4.5e+09 4.583e+09 ... 5e+09\n",
       "    t1                    (main_dim) float64 56B 4.238e-05 ... 4.154e-05\n",
       "Attributes:\n",
       "    tuid:                      20250723-134500-222-c5d165\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 2kB\n",
       "Dimensions:               \u001b[1m(\u001b[0mmain_dim: \u001b[1;36m7\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "  * main_dim              \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m object 56B MultiIndex\n",
       "  * flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 56B \u001b[1;36m-0.04\u001b[0m \u001b[1;36m-0.02667\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.02667\u001b[0m \u001b[1;36m0.04\u001b[0m\n",
       "  * resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "Attributes:\n",
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<xarray.DataArray 'qubit_freq' (main_dim: 1)> Size: 8B\n",
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<xarray.Dataset> Size: 2kB\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "    flux_bias             (main_dim) float64 56B -0.04 -0.02667 ... 0.02667 0.04\n",
       "    resonator_freq_tuids  (main_dim) <U26 728B '20250723-134500-220-0ee468' ....\n",
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       "Attributes:\n",
       "    tuid:                      20250723-134500-222-c5d165\n",
       "    dataset_name:              \n",
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       "Coordinates:\n",
       "    flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 56B \u001b[1;36m-0.04\u001b[0m \u001b[1;36m-0.02667\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.02667\u001b[0m \u001b[1;36m0.04\u001b[0m\n",
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     "execution_count": 16,
     "metadata": {},
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   "source": [
    "all(dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids == dataset.t1_tuids)"
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   "id": "27741c5c",
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    "But, for example, the `dtype` has been changed to `object`\n",
    "(from fixed-length string):"
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "62cb3085",
   "metadata": {},
   "outputs": [
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dataset.t1_tuids.dtype == dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids.dtype"
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